Relevant bibliographies by topics / Music information retrieval

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Music information retrieval'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Music information retrieval"

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Downie, J. Stephen. "Music information retrieval." Annual Review of Information Science and Technology 37, no. 1 (2005): 295–340. http://dx.doi.org/10.1002/aris.1440370108.

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Knees, Peter, Markus Schedl, and Òscar Celma. "Hybrid music information retrieval." International Journal of Multimedia Information Retrieval 2, no. 1 (2013): 1–2. http://dx.doi.org/10.1007/s13735-013-0033-9.

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Weissenberger, Lynnsey. "Toward a universal, meta-theoretical framework for music information classification and retrieval." Journal of Documentation 71, no. 5 (2015): 917–37. http://dx.doi.org/10.1108/jd-08-2013-0106.

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Purpose – The purpose of this paper is to present a new framework for representing music for information retrieval that emphasizes socio-cultural aspects of music. Design/methodology/approach – Philosophical and theoretical concepts related to the nature of music, aboutness, musical works are explored as they inform how music is represented. Multidisciplinary perspectives on music information representation, classification, and retrieval provide insight into how information science can better accommodate music information within its disciplinary boundaries. Findings – A new term, music informa
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OKUNO, Hiroshi G., Tetsuro KITAHARA, and Kazuyoshi YOSHII. "Music Feature Extraction and Music Information Retrieval." Journal of The Institute of Electrical Engineers of Japan 127, no. 7 (2007): 417–20. http://dx.doi.org/10.1541/ieejjournal.127.417.

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de Haas, W. Bas, and Frans Wiering. "Hooked on Music Information Retrieval." Empirical Musicology Review 5, no. 4 (2010): 176–85. http://dx.doi.org/10.18061/1811/48551.

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Tao Li and M. Ogihara. "Toward intelligent music information retrieval." IEEE Transactions on Multimedia 8, no. 3 (2006): 564–74. http://dx.doi.org/10.1109/tmm.2006.870730.

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Urbano, Julián, Markus Schedl, and Xavier Serra. "Evaluation in Music Information Retrieval." Journal of Intelligent Information Systems 41, no. 3 (2013): 345–69. http://dx.doi.org/10.1007/s10844-013-0249-4.

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Downie, J. Stephen. "The music information retrieval evaluation exchange (2005–2007): A window into music information retrieval research." Acoustical Science and Technology 29, no. 4 (2008): 247–55. http://dx.doi.org/10.1250/ast.29.247.

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Lee, Kuo Ying, and Chih Ming Hu. "Research on the Development of Music Information Retrieval and Fuzzy Search." Scientific and Social Research 4, no. 4 (2022): 1–10. http://dx.doi.org/10.26689/ssr.v4i4.3771.

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With the popularization of modern broadband networks, many network resources are serving as media for the public to seek knowledge. In order to help users avoid spending hours searching for music-related information, establishing an efficient multimedia database is the main goal of the music retrieval system. Network music retrieval users are usually unfamiliar with the songs and can only remember a portion of the music track, so it is important to develop a fuzzy algorithm in music search. In this research, the function and frame of various current music retrieval systems are discussed, a com
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Jun, Sanghoon, Seungmin Rho, and Eenjun Hwang. "Music Retrieval and Recommendation Scheme Based on Varying Mood Sequences." International Journal on Semantic Web and Information Systems 6, no. 2 (2010): 1–16. http://dx.doi.org/10.4018/jswis.2010040101.

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A typical music clip consists of one or more segments with different moods and such mood information could be a crucial clue for determining the similarity between music clips. One representative mood has been selected for music clip for retrieval, recommendation or classification purposes, which often gives unsatisfactory result. In this paper, the authors propose a new music retrieval and recommendation scheme based on the mood sequence of music clips. The authors first divide each music clip into segments through beat structure analysis, then, apply the k-medoids clustering algorithm for gr
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Dissertations / Theses on the topic "Music information retrieval"

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Koenders, Michael. "FROM MUSIC INFORMATION RETRIEVAL (MIR) TO INFORMATION RETRIEVAL FOR MUSIC (IRM)." Thesis, The University of Sydney, 2016. http://hdl.handle.net/2123/16914.

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This thesis reviews and discusses certain techniques from the domain of (Music) Information Retrieval, in particular some general data mining algorithms. It also describes their specific adaptations for use as building blocks in the CACE4 software application. The use of Augmented Transition Networks (ATN) from the field of (Music) Information Retrieval is, to a certain extent, adequate as long as one keeps the underlying tonal constraints and rules as a guide to understanding the structure one is looking for. However since a large proportion of algorithmic music, including music composed by t
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Shao, Bo. "User-centric Music Information Retrieval." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/416.

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The rapid growth of the Internet and the advancements of the Web technologies have made it possible for users to have access to large amounts of on-line music data, including music acoustic signals, lyrics, style/mood labels, and user-assigned tags. The progress has made music listening more fun, but has raised an issue of how to organize this data, and more generally, how computer programs can assist users in their music experience. An important subject in computer-aided music listening is music retrieval, i.e., the issue of efficiently helping users in locating the music they are looking for
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Boland, Daniel. "Engaging with music retrieval." Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/6727/.

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Music collections available to listeners have grown at a dramatic pace, now spanning tens of millions of tracks. Interacting with a music retrieval system can thus be overwhelming, with users offered ‘too-much-choice’. The level of engagement required for such retrieval interactions can be inappropriate, such as in mobile or multitasking contexts. Music recommender systems are widely employed to address this issue, however tend toward the opposite extreme of disempowering users and suffer from issues of subjectivity and confounds, such as the equalisation of tracks. This challenge and the styl
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Page, Stephen Dowland. "Computer tools for music information retrieval." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293399.

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Pérez-Sancho, Carlos. "Stochastic language models for music information retrieval." Doctoral thesis, Universidad de Alicante, 2009. http://hdl.handle.net/10045/14217.

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Pearce, Alastair T. P. "MAP : a computer program for music information retrieval." Thesis, King's College London (University of London), 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264938.

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Martinek, Václav. "Rozpoznávání hudebních coververzí pomocí technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442587.

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This master’s thesis deals with designs and implementation of systems for music cover recognition. The introduction part is devoted to the calculation parameters from audio signal using Music Information Retrieval techniques. Subsequently, various forms of cover versions and musical aspects that cover versions share are defined. The thesis also deals in detail with the creation and distribution of a database of cover versions. Furthermore, the work presents methods and techniques for comparing and processing the calculated parameters. Attention is then paid to the OTI method, CSM calculation a
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Zemánková, Šárka. "Rozpoznávání hudebního žánru za pomoci technik Music Information Retrieval." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2019. http://www.nusl.cz/ntk/nusl-401988.

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This diploma work deals with music genre recognition using the techniques of Music Information Retrieval. It contains a brief description of the principle of this research area and its subfield called Music Genre Recognition. The following chapter includes selection of the most suitable parameters for describing music genres. This work further characterizes machine learning methods used in this field of research. The next chapter deals with the descriptions of music datasets created for genre classification studies. Subsequently, there is a draft and evaluation of the system for music genre re
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Deng, Jie. "Emotion-based music retrieval and recommendation." HKBU Institutional Repository, 2014. https://repository.hkbu.edu.hk/etd_oa/82.

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The digital music industry has expanded dramatically during the past decades, which results in the generation of enormous amounts of music data. Along with the Internet, the growing volume of quantitative data about users (e.g., users’ behaviors and preferences) can be easily collected nowadays. All these factors have the potential to produce big data in the music industry. By utilizing big data analysis of music related data, music can be better semantically understood (e.g., genres and emotions), and the user’s high-level needs such as automatic recognition and annotation can be satisfied. F
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Reed, Jeremy T. "Acoustic segment modeling and preference ranking for music information retrieval." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37189.

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This dissertation focuses on improving content-based recommendation systems for music. Specifically, progress in the development in music content-based recommendation systems has stalled in recent years due to some faulty assumptions: 1. most acoustic content-based systems for music information retrieval (MIR) assume a bag-of-frames model, where it is assumed that a song contains a simplistic, global audio texture 2. genre, style, mood, and authors are appropriate categories for machine-oriented recommendation 3. similarity is a universal construct and does not vary among different users The
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Books on the topic "Music information retrieval"

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Raś, Zbigniew W., and Alicja A. Wieczorkowska, eds. Advances in Music Information Retrieval. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11674-2.

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Müller, Meinard. Information Retrieval for Music and Motion. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74048-3.

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Müller, Meinard. Information retrieval for music and motion. Springer, 2007.

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Boehm, Carola. MuTaTeD'II: A system for music information retrieval of encoded music. Resources: Council for Museums, Archives and Libraries, 2001.

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France) International Symposium on Music Information Retrieval (3rd 2002 Paris. ISMIR 2002 tutorial: Music information retrieval for audio signals. IRCAM-Centre Pompidou, 2002.

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Harrold, Ann. Musaurus: A music thesaurus : a new approach to organising music information. Music Press, 1991.

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London), International Conference on Music Information Retrieval (6th 2005 University of. ISMIR 2005: 6th International Conference on Music Information Retrieval : Proceedings : variation 2 : Queen Mary, University of London & Goldsmiths College, University of London, 11-15 September, 2005. Queen Mary, University of London, 2005.

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Pa.) International Conference on Music Information Retrieval (9th 2008 Philadelphia. ISMIR 2008: Proceedings of the 9th International Conference of Music Information Retrieval. [Drexel University], 2008.

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Yang, Yi-Hsuan. Music emotion recognition. CRC, 2011.

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Condat, Jean-Bernard. DIALOG--l'information musicologique immédiate. J.-B. Condat, 1987.

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Book chapters on the topic "Music information retrieval"

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Gómez, Emilia, Helena Cuesta, Aggelos Gkiokas, Juan Gómez-Cañón, Lorenzo Porcaro, and Furkan Yesiler. "Audio-based Music Retrieval." In Information Retrieval. ACM, 2024. https://doi.org/10.1145/3674127.3702959.

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Datta, Asoke Kumar, Sandeep Singh Solanki, Ranjan Sengupta, Soubhik Chakraborty, Kartik Mahto, and Anirban Patranabis. "Music Information Retrieval." In Signals and Communication Technology. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3959-1_2.

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Burgoyne, John Ashley, Ichiro Fujinaga, and J. Stephen Downie. "Music Information Retrieval." In A New Companion to Digital Humanities. John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118680605.ch15.

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Wang, Ju-Chiang, Yi-Hsuan Yang, and Hsin-Min Wang. "Affective Music Information Retrieval." In Human–Computer Interaction Series. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31413-6_12.

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Li, Jing, Hongfei Lin, and Lijuan Zhou. "Emotion Tag Based Music Retrieval Algorithm." In Information Retrieval Technology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17187-1_56.

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Kostek, Bożena. "Music Information Retrieval in Music Repositories." In Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30344-9_17.

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Lu, Kai, Guanyuan Zhang, Rui Li, Shuai Zhang, and Bin Wang. "Exploiting and Exploring Hierarchical Structure in Music Recommendation." In Information Retrieval Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35341-3_18.

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Weihs, Claus, Klaus Friedrichs, Markus Eichhoff, and Igor Vatolkin. "Software in Music Information Retrieval." In Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24466-7_43.

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Li, Qing, Sung Hyon Myaeng, Dong Hai Guan, and Byeong Man Kim. "A Probabilistic Model for Music Recommendation Considering Audio Features." In Information Retrieval Technology. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11562382_6.

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Kaliciak, Leszek, Ben Horsburgh, Dawei Song, Nirmalie Wiratunga, and Jeff Pan. "Enhancing Music Information Retrieval by Incorporating Image-Based Local Features." In Information Retrieval Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35341-3_19.

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Conference papers on the topic "Music information retrieval"

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Wu, Junda, Zachary Novack, Amit Namburi, et al. "FUTGA-MIR: Enhancing Fine-grained and Temporally-aware Music Understanding with Music Information Retrieval." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888485.

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Shatri, Elona, and György Fazekas. "Knowledge Discovery in Optical Music Recognition: Enhancing Information Retrieval with Instance Segmentation." In 16th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0012947500003838.

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Louro, Pedro, Guilherme Branco, Hugo Redinho, et al. "MERGE App: A Prototype Software for Multi-User Emotion-Aware Music Management." In 16th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013067800003838.

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Tzanetakis, George. "Music information retrieval." In the seventeen ACM international conference. ACM Press, 2009. http://dx.doi.org/10.1145/1631272.1631450.

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Oliveira, Gabriel P., Gabriel R. G. Barbosa, Bruna C. Melo, Mariana O. Silva, Danilo B. Seufitelli, and Mirella M. Moro. "MUHSIC: An Open Dataset with Temporal Musical Success Information." In Dataset Showcase Workshop. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/dsw.2021.17415.

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Music is an alive industry with an increasing volume of complex data that creates new challenges and opportunities for extracting knowledge, benefiting not only the different music segments but also the Music Information Retrieval (MIR) community. In this paper, we present MUHSIC, a novel dataset with enhanced information on musical success. We focus on artists and genres by combining chart-related data with acoustic metadata to describe the temporal evolution of musical careers. The enriched and curated data allow building success-based time series to investigate high-impact periods (hot stre
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Kogo, Kenji, Kyoji Kawagoe, and Teruhisa Hochin. "Music similarity retrieval method considering music arrangement." In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016. http://dx.doi.org/10.1109/icis.2016.7550853.

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Raimond, Yves, Christopher Sutton, and Mark Sandler. "A distributed data space for music-related information." In Workshop on multimedia information retrieval. ACM Press, 2007. http://dx.doi.org/10.1145/1290067.1290081.

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Bozkurt, Baris, Ali Cenk Gedik, and M. Kemal Karaosmanoglu. "Music information retrieval for Turkish music: problems, solutions and tools." In 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU). IEEE, 2009. http://dx.doi.org/10.1109/siu.2009.5136518.

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Knees, Peter, and Markus Schedl. "Music Retrieval and Recommendation." In SIGIR '15: The 38th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2015. http://dx.doi.org/10.1145/2766462.2767880.

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Knees, Peter, and Markus Schedl. "Music similarity and retrieval." In SIGIR '13: The 36th International ACM SIGIR conference on research and development in Information Retrieval. ACM, 2013. http://dx.doi.org/10.1145/2484028.2484193.

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Reports on the topic "Music information retrieval"

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Sun, Chenbing, Zhe Wang, and Yuening Dai. Music therapy for sleep quality in cancer patients with insomnia：A protocol for systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.12.0128.

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Review question / Objective: The aim of this systematic review is to compare music therapy in terms of efficacy in cancer patients with insomnia disorders to better inform clinical practice. Condition being studied: The effectiveness of music therapy for cancer- associate insomnia is the main interest of this systematic review. Information sources: MEDLINE (PubMed, Ovid) The Cochrane Library, Web of Science, Embase and Electronic retrieval of Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CHKD-CNKI), VIP database, Wanfang Database will be searched from i
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